AI 中文总结
研究针对多角色交互动画难题，提出MultiAnimate框架，通过特定身份参考网络、身份感知姿态编码器和交互引导模块，能在共享环境中同时对多角色动画制作，保持身份与空间关系，实验证明其在多角色动画尤其是复杂运动序列场景中的优越性。
AI 中文摘要
生成模型的进展和技术创新显著解决了角色图像动画的基本挑战。但现有方法主要聚焦单参考图像的角色动画，限制了其在多角色交互动画等场景的应用。本文介绍了MultiAnimate框架，可在共享环境中同时对多个角色进行动画制作，保持身份一致性和空间关系。该框架通过多种机制实现目标，包括特定身份参考网络、身份感知姿态编码器和交互引导模块。大量实验和消融分析证明了该框架在多角色动画方面的优越性，特别是在复杂运动序列场景中。
英文摘要
Recent advances in generative models and technological innovations have significantly addressed the fundamental challenges of character image animation. However, existing approaches predominantly focus on character animation from a single reference image, substantially limiting their applicability in scenarios such as multiple character interaction animation. To fill this gap, this paper introduces MultiAnimate, a comprehensive framework that enables concurrent animation of multiple characters within a shared environment while preserving both identity consistency and spatial relationships. The framework achieves these objectives through multiple well-designed mechanisms. First, we incorporate an identity-specific reference net that enables appearance extraction from multiple reference images, distinguishing MultiAnimate from existing approaches constrained to single reference inputs. Second, we implement an identity-aware pose encoder to address the character-pose binding challenge, wherein an attention mechanism enables the network to accurately differentiate and process multiple pose sequences during generation. Third, we introduce an interaction guider module that enhances the framework's capability to handle complex inter-character interactions by leveraging character-specific mask information, serving as an optional component that refines the pose sequences. Extensive experiments and ablation analyses demonstrate our framework's superiority in multiple character animation, particularly in scenarios involving complex motion sequences.
CommentsPreprint, under review
